In this article, after a brief introduction to efficient market hypothesis and the very basics of technical analysis, we implement two different trading strategies based on moving averages and few oscillators on a wide set of equity indexes in various countries. The aim is to investigate whether efficient market hypothesis holds across markets of very different areas.
The efficient markets hypothesis
Efficient Market Hypothesis (EMH) is one of the cornerstones of financial economics. First presented by Professor Eugene Fama in 1970, EMH states that any financial asset traded in the market is always traded at its fair value, thus making impossible for investors to identify undervalued and overvalued securities and to anticipate future market trends. As soon as any new information comes in, prices immediately adjust accordingly.
The definition of EMH strictly depends on the connotation attributed to the term “fair value”. Indeed, the set of information that is meant to be used to determine the fair value of an asset can vary a lot. Therefore, three main variants of the EMH are introduced: weak, semi-strong and strong forms. In the case of weak form efficiency, the information set is composed by all data of the past market prices of the asset. In the case of semi-strong form efficiency, the information set is given by all the publicly available information, both microeconomic (dividend policy, earnings …) and macroeconomic (interest rates, inflation, exchange rates …). Finally, in the case of strong form efficiency, the market prices incorporate all the public and private information available.
Financial markets lack of strong form efficiency. As a matter of fact, the existence of insider trading is evidence that it is possible to profit from the availability of private information.
Semi-strong market efficiency is still matter of academic debate. As a general rule, the most developed and liquid financial markets enjoy semi-strong form efficiency, while often the same cannot be said about more peripheral and small markets. For instance, a large literature shows how the US markets efficiently price earnings announcements, stock splits, FED announcements as soon as the information is made public, even though some limited grey areas of inefficiency remain (e.g. IPO announcements, value anomaly, size anomaly). The presence of semi-strong form efficiency implies that fundamental analysis cannot produce consistent excess returns.
For what concerns weak form efficiency, the overwhelming majority of the academic works shows how efficiency holds in the major financial markets and in the more peripheral as well. The most basic test for weak efficiency consists in looking at the autocorrelations of market returns. In every relevant market the autocorrelation coefficients are not statistically different from zero.
Nevertheless, some contradictory results are found when more sophisticated manipulations of past prices are used to obtain signals on future market trends. For instance, in 1992 Brock, Lakonishok and LeBaron showed that, by implementing a strategy based on support-resistance levels and moving averages to produce trend inversion signals, it was possible to earn consistent excess returns on the Dow Jones from 1897 to 1986.
In addition to that, there are a number of anomalies that cast some doubts about the weak form efficiency of the major financial markets. The most significant examples are the “weekend effect” (statistically significant differences in market returns across different days of the week, with Mondays usually showing weaker performances than the second part of the week) and the “January effect” (stocks, and in particular small cap stocks, show significant better returns in January with respect to the rest of the year). These anomalies, that can be detected also in the most developed and liquid markets, find their explanation in behavioural finance in the first case and in fiscal considerations for the asset managers in the second case.
Although the theoretical credentials of trading rules are very weak, the reason why in some cases they might work is a very intriguing topic. The exploitation of these inefficiencies in the markets is the goal of technical analysis.
An introduction to technical analysis
Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends. Therefore, the instruments available to the analyst are basically historical data concerning prices and volumes. Technical analysis is in open contradiction to all the forms of market efficiency, as the latter implies that the current price of a security already incorporates all available data, so that it is not possible to systematically beat the market through the study of historical prices’ patterns.
On the contrary, the technician philosophy is based on three pillars. The first one is that market action discounts everything. This makes sense if you think that, given that everything possibly affecting the price (fundamentals, macro events…) is already discounted, a study of the price action is all that is needed. The second premise to the technical approach is that prices move in trends: the job of the technician is to spot new trends in their early stage. However, to discover trends and patterns there is the need for the last pillar: history repeats itself. Indeed, chart patterns and oscillators values that categorise buy or sell signals are derived from historical series. Since they worked well in the past, the technician just assumes they will work in the future as well. It is worth noting how an upward trend is not characterized by a straight positive-sloping line, but due to the nature of the markets it will be defined as a series of successively higher peaks and troughs in a given period of time.
The most widely spread ways a technician adopts to spot trends in early stages are chart patterns, moving averages and oscillators or indicators. The former are formations which appear on price charts and that have predicted value. Price patterns fall mainly in two categories: reversal (the trend in act is due to reverse) and continuation (the trend gains strength from the formation on the graph and continues its course). Examples of patterns are the “Head and Shoulders” (reversal) and the “Flags” and “Pennants” (continuation). Nonetheless, patterns are to be spotted individually by a human because of their nature, so they are not suitable for an automated trading strategy. Moving averages’ description is left to the next paragraph since they are part of the strategy we adopted. The last category of tools proves useful for the purpose of trading strategy creation: oscillators and indicators. It is crucial to note that these are to be used along with basic trend analysis, because in a sideway market situation they might provide false buy or sell signals. Most oscillators are plotted under the price chart and resemble a flat horizontal band. Generally, peak and troughs in the oscillator chart coincide with those on the price chart. Indeed, the signal the analyst tries to get from an oscillator is that when the oscillator reaches an extreme value, this signals that price may have moved too fast and it is now due for a correction. Examples of oscillators are the Relative Strength Index (RSI) and the Stochastics, which are part of the adopted strategy.
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